Item Details

Bayesian Estimation and the Kalman Filter

Barker, Allen; Brown, Donald; Martin, Worthy
Format
Report
Author
Barker, Allen
Brown, Donald
Martin, Worthy
Abstract
In this tutorial article we give a Bayesian derivation of a basic state estimation result for discrete-time Markov process models with independent process and measurement noise and measurements not affecting the state. We then list some properties of Gaussian random vectors and show how the Kalman filtering algorithm follows from the general state estimation result and a linear-Gaussian model definition. We give some illustrative examples including a probabilistic Turing machine, dynamic classification, and tracking a moving object.
Language
English
Date Received
20121030
Published
University of Virginia, Institute for Parallel Computation, 1994
Published Date
1994
Collection
Libra Open Repository
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